Overview

Dataset statistics

Number of variables31
Number of observations41174
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory171.0 B

Variable types

Numeric11
Categorical19
Boolean1

Warnings

df_index is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with df_index and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 3 other fieldsHigh correlation
euribor3m is highly correlated with df_index and 3 other fieldsHigh correlation
nr.employed is highly correlated with df_index and 4 other fieldsHigh correlation
df_index is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with df_index and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 1 other fieldsHigh correlation
euribor3m is highly correlated with df_index and 2 other fieldsHigh correlation
nr.employed is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with cons.price.idxHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 1 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with pdays and 9 other fieldsHigh correlation
age is highly correlated with job_retiredHigh correlation
pdays is highly correlated with nr.employed and 5 other fieldsHigh correlation
cons.conf.idx is highly correlated with nr.employed and 9 other fieldsHigh correlation
subscribed is highly correlated with nr.employed and 3 other fieldsHigh correlation
month is highly correlated with nr.employed and 6 other fieldsHigh correlation
df_index is highly correlated with nr.employed and 9 other fieldsHigh correlation
cons.price.idx is highly correlated with nr.employed and 7 other fieldsHigh correlation
emp.var.rate is highly correlated with nr.employed and 7 other fieldsHigh correlation
job_retired is highly correlated with ageHigh correlation
contact is highly correlated with nr.employed and 6 other fieldsHigh correlation
previous is highly correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly correlated with nr.employed and 7 other fieldsHigh correlation
euribor3m is highly correlated with nr.employed and 10 other fieldsHigh correlation
month is highly correlated with contactHigh correlation
job_blue-collar is highly correlated with educationHigh correlation
education is highly correlated with job_blue-collarHigh correlation
contact is highly correlated with monthHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
previous has 35549 (86.3%) zeros Zeros

Reproduction

Analysis started2021-07-19 11:16:43.677581
Analysis finished2021-07-19 11:17:32.140488
Duration48.46 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct41174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20593.43411
Minimum0
Maximum41187
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:32.342339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2059.65
Q110295.25
median20595.5
Q330890.75
95-th percentile39128.35
Maximum41187
Range41187
Interquartile range (IQR)20595.5

Descriptive statistics

Standard deviation11890.5728
Coefficient of variation (CV)0.5773963067
Kurtosis-1.200140335
Mean20593.43411
Median Absolute Deviation (MAD)10298
Skewness-4.76413043 × 10-7
Sum847914056
Variance141385721.4
MonotonicityStrictly increasing
2021-07-19T07:17:32.622105image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
232941
 
< 0.1%
354921
 
< 0.1%
334451
 
< 0.1%
395901
 
< 0.1%
375431
 
< 0.1%
109281
 
< 0.1%
88811
 
< 0.1%
150261
 
< 0.1%
129791
 
< 0.1%
Other values (41164)41164
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
411871
< 0.1%
411861
< 0.1%
411851
< 0.1%
411841
< 0.1%
411831
< 0.1%
411821
< 0.1%
411811
< 0.1%
411801
< 0.1%
411791
< 0.1%
411781
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02402001
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:32.891556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42083807
Coefficient of variation (CV)0.2603646026
Kurtosis0.7909874386
Mean40.02402001
Median Absolute Deviation (MAD)7
Skewness0.7845151491
Sum1647949
Variance108.593866
MonotonicityNot monotonic
2021-07-19T07:17:33.188742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311946
 
4.7%
321845
 
4.5%
331833
 
4.5%
361779
 
4.3%
351758
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391430
 
3.5%
Other values (68)24196
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24462
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

marital
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
married
25000 
single
11563 
divorced
4611 

Length

Max length8
Median length7
Mean length6.831155584
Min length6

Characters and Unicode

Total characters281266
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married25000
60.7%
single11563
28.1%
divorced4611
 
11.2%

Length

2021-07-19T07:17:33.667484image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:33.806708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
married25000
60.7%
single11563
28.1%
divorced4611
 
11.2%

Most occurring characters

ValueCountFrequency (%)
r54611
19.4%
i41174
14.6%
e41174
14.6%
d34222
12.2%
m25000
8.9%
a25000
8.9%
s11563
 
4.1%
n11563
 
4.1%
g11563
 
4.1%
l11563
 
4.1%
Other values (3)13833
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter281266
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r54611
19.4%
i41174
14.6%
e41174
14.6%
d34222
12.2%
m25000
8.9%
a25000
8.9%
s11563
 
4.1%
n11563
 
4.1%
g11563
 
4.1%
l11563
 
4.1%
Other values (3)13833
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin281266
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r54611
19.4%
i41174
14.6%
e41174
14.6%
d34222
12.2%
m25000
8.9%
a25000
8.9%
s11563
 
4.1%
n11563
 
4.1%
g11563
 
4.1%
l11563
 
4.1%
Other values (3)13833
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII281266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r54611
19.4%
i41174
14.6%
e41174
14.6%
d34222
12.2%
m25000
8.9%
a25000
8.9%
s11563
 
4.1%
n11563
 
4.1%
g11563
 
4.1%
l11563
 
4.1%
Other values (3)13833
 
4.9%

education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
basic
14242 
university.degree
12164 
high.school
9511 
professional.course
5239 
illiterate
 
18

Length

Max length19
Median length11
Mean length11.71467431
Min length5

Characters and Unicode

Total characters482340
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic
5th rowhigh.school

Common Values

ValueCountFrequency (%)
basic14242
34.6%
university.degree12164
29.5%
high.school9511
23.1%
professional.course5239
 
12.7%
illiterate18
 
< 0.1%

Length

2021-07-19T07:17:34.230403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:34.373293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
basic14242
34.6%
university.degree12164
29.5%
high.school9511
23.1%
professional.course5239
 
12.7%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e59170
12.3%
i53356
11.1%
s51634
10.7%
r34824
 
7.2%
o34739
 
7.2%
c28992
 
6.0%
h28533
 
5.9%
.26914
 
5.6%
g21675
 
4.5%
a19499
 
4.0%
Other values (10)123004
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter455426
94.4%
Other Punctuation26914
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59170
13.0%
i53356
11.7%
s51634
11.3%
r34824
 
7.6%
o34739
 
7.6%
c28992
 
6.4%
h28533
 
6.3%
g21675
 
4.8%
a19499
 
4.3%
n17403
 
3.8%
Other values (9)105601
23.2%
Other Punctuation
ValueCountFrequency (%)
.26914
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin455426
94.4%
Common26914
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59170
13.0%
i53356
11.7%
s51634
11.3%
r34824
 
7.6%
o34739
 
7.6%
c28992
 
6.4%
h28533
 
6.3%
g21675
 
4.8%
a19499
 
4.3%
n17403
 
3.8%
Other values (9)105601
23.2%
Common
ValueCountFrequency (%)
.26914
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII482340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59170
12.3%
i53356
11.1%
s51634
10.7%
r34824
 
7.2%
o34739
 
7.2%
c28992
 
6.0%
h28533
 
5.9%
.26914
 
5.6%
g21675
 
4.5%
a19499
 
4.0%
Other values (10)123004
25.5%

default
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
41171 
True
 
3
ValueCountFrequency (%)
False41171
> 99.9%
True3
 
< 0.1%
2021-07-19T07:17:34.480737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
cellular
26134 
telephone
15040 

Length

Max length9
Median length8
Mean length8.36527906
Min length8

Characters and Unicode

Total characters344432
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular26134
63.5%
telephone15040
36.5%

Length

2021-07-19T07:17:34.841949image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:34.967219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
cellular26134
63.5%
telephone15040
36.5%

Most occurring characters

ValueCountFrequency (%)
l93442
27.1%
e71254
20.7%
c26134
 
7.6%
u26134
 
7.6%
a26134
 
7.6%
r26134
 
7.6%
t15040
 
4.4%
p15040
 
4.4%
h15040
 
4.4%
o15040
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344432
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l93442
27.1%
e71254
20.7%
c26134
 
7.6%
u26134
 
7.6%
a26134
 
7.6%
r26134
 
7.6%
t15040
 
4.4%
p15040
 
4.4%
h15040
 
4.4%
o15040
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin344432
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l93442
27.1%
e71254
20.7%
c26134
 
7.6%
u26134
 
7.6%
a26134
 
7.6%
r26134
 
7.6%
t15040
 
4.4%
p15040
 
4.4%
h15040
 
4.4%
o15040
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII344432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l93442
27.1%
e71254
20.7%
c26134
 
7.6%
u26134
 
7.6%
a26134
 
7.6%
r26134
 
7.6%
t15040
 
4.4%
p15040
 
4.4%
h15040
 
4.4%
o15040
 
4.4%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
may
13766 
jul
7169 
aug
6175 
jun
5318 
nov
4100 
Other values (5)
4646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123522
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may13766
33.4%
jul7169
17.4%
aug6175
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Length

2021-07-19T07:17:35.383265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:35.536175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
may13766
33.4%
jul7169
17.4%
aug6175
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a23118
18.7%
u18662
15.1%
m14312
11.6%
y13766
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6175
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123522
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a23118
18.7%
u18662
15.1%
m14312
11.6%
y13766
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6175
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123522
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a23118
18.7%
u18662
15.1%
m14312
11.6%
y13766
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6175
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a23118
18.7%
u18662
15.1%
m14312
11.6%
y13766
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6175
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
thu
8618 
mon
8512 
wed
8133 
tue
8085 
fri
7826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123522
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8133
19.8%
tue8085
19.6%
fri7826
19.0%

Length

2021-07-19T07:17:35.994322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:36.130054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8133
19.8%
tue8085
19.6%
fri7826
19.0%

Most occurring characters

ValueCountFrequency (%)
t16703
13.5%
u16703
13.5%
e16218
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8133
6.6%
d8133
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123522
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t16703
13.5%
u16703
13.5%
e16218
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8133
6.6%
d8133
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123522
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t16703
13.5%
u16703
13.5%
e16218
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8133
6.6%
d8133
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t16703
13.5%
u16703
13.5%
e16218
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8133
6.6%
d8133
6.6%
f7826
6.3%
Other values (2)15652
12.7%

duration
Real number (ℝ≥0)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.3192063
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:36.565871image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.3106284
Coefficient of variation (CV)1.003837973
Kurtosis20.24282408
Mean258.3192063
Median Absolute Deviation (MAD)94
Skewness3.262730699
Sum10636035
Variance67242.00198
MonotonicityNot monotonic
2021-07-19T07:17:36.851675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90170
 
0.4%
85170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124163
 
0.4%
87162
 
0.4%
72161
 
0.4%
104161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39533
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
 
< 0.1%
530
 
0.1%
637
0.1%
754
0.1%
869
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
35091
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567931219
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:37.094271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770373431
Coefficient of variation (CV)1.078834749
Kurtosis36.97026038
Mean2.567931219
Median Absolute Deviation (MAD)1
Skewness4.761939714
Sum105732
Variance7.674968945
MonotonicityNot monotonic
2021-07-19T07:17:37.348036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117633
42.8%
210567
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117633
42.8%
210567
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.4630349
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:37.600648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9414685
Coefficient of variation (CV)0.1942323619
Kurtosis22.22023448
Mean962.4630349
Median Absolute Deviation (MAD)0
Skewness-4.921252447
Sum39628453
Variance34947.11265
MonotonicityNot monotonic
2021-07-19T07:17:37.879937image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99939659
96.3%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (17)205
 
0.5%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
546
 
0.1%
6412
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
99939659
96.3%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
 
< 0.1%
178
 
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1730218099
Minimum0
Maximum7
Zeros35549
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:38.078336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949749339
Coefficient of variation (CV)2.860766133
Kurtosis20.10105502
Mean0.1730218099
Median Absolute Deviation (MAD)0
Skewness3.831287716
Sum7124
Variance0.2450001852
MonotonicityNot monotonic
2021-07-19T07:17:38.315668image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035549
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035549
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035549
86.3%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
nonexistent
35549 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.45353864
Min length7

Characters and Unicode

Total characters430414
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent35549
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Length

2021-07-19T07:17:38.833423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:38.995705image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35549
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n106647
24.8%
e76723
17.8%
t71098
16.5%
i39801
 
9.2%
s39668
 
9.2%
o35549
 
8.3%
x35549
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430414
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n106647
24.8%
e76723
17.8%
t71098
16.5%
i39801
 
9.2%
s39668
 
9.2%
o35549
 
8.3%
x35549
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin430414
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n106647
24.8%
e76723
17.8%
t71098
16.5%
i39801
 
9.2%
s39668
 
9.2%
o35549
 
8.3%
x35549
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII430414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n106647
24.8%
e76723
17.8%
t71098
16.5%
i39801
 
9.2%
s39668
 
9.2%
o35549
 
8.3%
x35549
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08186476903
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17186
Negative (%)41.7%
Memory size321.8 KiB
2021-07-19T07:17:39.138516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570899323
Coefficient of variation (CV)19.18895444
Kurtosis-1.062807281
Mean0.08186476903
Median Absolute Deviation (MAD)0.3
Skewness-0.7239852583
Sum3370.7
Variance2.467724684
MonotonicityNot monotonic
2021-07-19T07:17:39.368083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416227
39.4%
-1.89182
22.3%
1.17761
18.8%
-0.13682
 
8.9%
-2.91662
 
4.0%
-3.41070
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41070
 
2.6%
-3172
 
0.4%
-2.91662
 
4.0%
-1.89182
22.3%
-1.7773
 
1.9%
-1.1635
 
1.5%
-0.210
 
< 0.1%
-0.13682
 
8.9%
1.17761
18.8%
1.416227
39.4%
ValueCountFrequency (%)
1.416227
39.4%
1.17761
18.8%
-0.13682
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%
-1.7773
 
1.9%
-1.89182
22.3%
-2.91662
 
4.0%
-3172
 
0.4%
-3.41070
 
2.6%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57571293
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:39.580249image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5788490096
Coefficient of variation (CV)0.006185889388
Kurtosis-0.8299137705
Mean93.57571293
Median Absolute Deviation (MAD)0.38
Skewness-0.2308249341
Sum3852886.404
Variance0.3350661759
MonotonicityNot monotonic
2021-07-19T07:17:39.843411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947761
18.8%
93.9186681
16.2%
92.8935793
14.1%
93.4445172
12.6%
94.4654374
10.6%
93.23615
8.8%
93.0752457
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431446
 
1.1%
92.469177
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935793
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947761
18.8%
93.9186681
16.2%
93.876212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.5030699
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41174
Negative (%)100.0%
Memory size321.8 KiB
2021-07-19T07:17:40.086468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.62787732
Coefficient of variation (CV)-0.1142599149
Kurtosis-0.3589891849
Mean-40.5030699
Median Absolute Deviation (MAD)4.4
Skewness0.3029833863
Sum-1667673.4
Variance21.41724849
MonotonicityNot monotonic
2021-07-19T07:17:40.329650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47761
18.8%
-42.76681
16.2%
-46.25793
14.1%
-36.15172
12.6%
-41.84374
10.6%
-423615
8.8%
-47.12457
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12457
 
6.0%
-46.25793
14.1%
-45.910
 
< 0.1%
-42.76681
16.2%
-423615
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9446
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6177
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15172
12.6%
-36.47761
18.8%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621230752
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:40.592830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734455757
Coefficient of variation (CV)0.4789685816
Kurtosis-1.406900443
Mean3.621230752
Median Absolute Deviation (MAD)0.108
Skewness-0.7091173401
Sum149100.555
Variance3.008336774
MonotonicityNot monotonic
2021-07-19T07:17:40.886209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572867
 
7.0%
4.9622611
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651070
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24626
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968991
 
2.4%
4.967642
 
1.6%
4.966620
 
1.5%
4.9651070
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622611
6.3%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.032805
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2021-07-19T07:17:41.089933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25239547
Coefficient of variation (CV)0.01398334367
Kurtosis-0.003697318809
Mean5167.032805
Median Absolute Deviation (MAD)37.1
Skewness-1.044252858
Sum212747408.7
Variance5220.408652
MonotonicityNot monotonic
2021-07-19T07:17:41.310295image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116227
39.4%
5099.18532
20.7%
51917761
18.8%
5195.83682
 
8.9%
5076.21662
 
4.0%
5017.51070
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
 
1.5%
4991.6773
 
1.9%
5008.7650
 
1.6%
5017.51070
 
2.6%
5023.5172
 
0.4%
5076.21662
 
4.0%
5099.18532
20.7%
5176.310
 
< 0.1%
51917761
18.8%
5195.83682
8.9%
ValueCountFrequency (%)
5228.116227
39.4%
5195.83682
 
8.9%
51917761
18.8%
5176.310
 
< 0.1%
5099.18532
20.7%
5076.21662
 
4.0%
5023.5172
 
0.4%
5017.51070
 
2.6%
5008.7650
 
1.6%
4991.6773
 
1.9%

subscribed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
36535 
1
4639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

Length

2021-07-19T07:17:41.726456image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:41.851475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036535
88.7%
14639
 
11.3%

has_loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
1
24126 
0
17048 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
124126
58.6%
017048
41.4%

Length

2021-07-19T07:17:42.241509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:42.366448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
124126
58.6%
017048
41.4%

Most occurring characters

ValueCountFrequency (%)
124126
58.6%
017048
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
124126
58.6%
017048
41.4%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
124126
58.6%
017048
41.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124126
58.6%
017048
41.4%

job_admin.
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
30425 
1
10749 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

Length

2021-07-19T07:17:42.760238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:42.883048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

Most occurring characters

ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030425
73.9%
110749
 
26.1%

job_blue-collar
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
31921 
1
9253 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

Length

2021-07-19T07:17:43.239263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:43.365034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

Most occurring characters

ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
031921
77.5%
19253
 
22.5%

job_entrepreneur
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
39718 
1
 
1456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

Length

2021-07-19T07:17:43.755535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:43.881606image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

Most occurring characters

ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039718
96.5%
11456
 
3.5%

job_housemaid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
40114 
1
 
1060

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

Length

2021-07-19T07:17:44.271165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:44.395125image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

Most occurring characters

ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040114
97.4%
11060
 
2.6%

job_management
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
38250 
1
 
2924

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

Length

2021-07-19T07:17:44.774971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:44.900334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

Most occurring characters

ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
038250
92.9%
12924
 
7.1%

job_retired
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
39456 
1
 
1718

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%

Length

2021-07-19T07:17:45.287845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:45.412831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%

Most occurring characters

ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039456
95.8%
11718
 
4.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
39753 
1
 
1421

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

Length

2021-07-19T07:17:45.845394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:45.980087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

Most occurring characters

ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039753
96.5%
11421
 
3.5%

job_services
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
37207 
1
3967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

Length

2021-07-19T07:17:46.412059image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:46.554048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

Most occurring characters

ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037207
90.4%
13967
 
9.6%

job_student
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
40299 
1
 
875

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

Length

2021-07-19T07:17:46.995086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:47.118372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

Most occurring characters

ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040299
97.9%
1875
 
2.1%

job_technician
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
34437 
1
6737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

Length

2021-07-19T07:17:47.813641image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:47.939128image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

Most occurring characters

ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034437
83.6%
16737
 
16.4%

job_unemployed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
0
40160 
1
 
1014

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Length

2021-07-19T07:17:48.333455image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-19T07:17:48.459478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Most occurring characters

ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41174
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common41174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040160
97.5%
11014
 
2.5%

Interactions

2021-07-19T07:17:03.404408image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:03.649571image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:03.986863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:04.192597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:04.396815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:04.612373image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:04.822287image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:05.019737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:05.220656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:05.418260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:05.615486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:05.813207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:06.033859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:06.276113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:06.501764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:06.725810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:06.947318image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:07.180498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:07.400751image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:07.624449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:07.847264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:08.069492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:08.292149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:08.538727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:08.778974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:08.993196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:09.202018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:09.419072image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:09.753218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:10.013893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:10.251137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:10.465106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:10.673415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:10.880604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:11.093252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:11.315504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:11.526093image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:11.734345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:11.948910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:12.178210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:12.385147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:12.596378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:12.804182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:13.010452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:13.234659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:13.464816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:13.683085image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:13.896188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:14.119336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:14.335284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:14.562891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:14.773701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:15.013794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:15.238503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:15.472000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:15.702678image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:15.942108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:16.208155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:16.437779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:16.670003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:16.901859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:17.305766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:17.527473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:17.753119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:17.970629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:18.197467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:18.449193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:18.673485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:18.922144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:19.134214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:19.351267image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:19.579154image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:19.818179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:20.040900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:20.239604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:20.493978image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:20.703779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:20.913123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:21.136656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:21.389265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:21.625706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:21.850304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:22.085342image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:22.298640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:22.503754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:22.705470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:22.905441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:23.103220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:23.301818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:23.503151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:23.719304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:23.922089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:24.128145image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:24.332051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:24.543248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:24.739230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:24.943886image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:25.147340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:25.345775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:25.544069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:25.745246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:25.966136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:26.166354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:26.547841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:26.751242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:26.963715image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:27.160765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:27.359514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:27.558338image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:27.752364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:27.951343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:28.150631image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:28.365800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:28.572699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:28.773355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:28.976491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:29.185862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:29.381575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:29.582863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:29.780657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-19T07:17:29.979130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-07-19T07:17:48.740876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-19T07:17:49.223584image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-19T07:17:49.703180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-19T07:17:50.228940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-19T07:17:50.701604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-19T07:17:30.740204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-19T07:17:31.741478image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagemaritaleducationdefaultcontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedsubscribedhas_loanjob_admin.job_blue-collarjob_entrepreneurjob_housemaidjob_managementjob_retiredjob_self-employedjob_servicesjob_studentjob_technicianjob_unemployed
0056marriedbasicnotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.00000010000000
1157marriedhigh.schoolnotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.00000000001000
2237marriedhigh.schoolnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.00100000001000
3340marriedbasicnotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.00010000000000
4456marriedhigh.schoolnotelephonemaymon30719990nonexistent1.193.994-36.44.8575191.00100000001000
5545marriedbasicnotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.00000000001000
6659marriedprofessional.coursenotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.00010000000000
7741marriedbasicnotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.00001000000000
8824singleprofessional.coursenotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.00100000000010
9925singlehigh.schoolnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.00100000001000

Last rows

df_indexagemaritaleducationdefaultcontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedsubscribedhas_loanjob_admin.job_blue-collarjob_entrepreneurjob_housemaidjob_managementjob_retiredjob_self-employedjob_servicesjob_studentjob_technicianjob_unemployed
411644117862marrieduniversity.degreenocellularnovthu483263success-1.194.767-50.81.0314963.61000000100000
411654117964divorcedprofessional.coursenocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.60100000100000
411664118036marrieduniversity.degreenocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.60010000000000
411674118137marrieduniversity.degreenocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.61110000000000
411684118229singlebasicnocellularnovfri112191success-1.194.767-50.81.0284963.60100000000001
411694118373marriedprofessional.coursenocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.61100000100000
411704118446marriedprofessional.coursenocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.60001000000000
411714118556marrieduniversity.degreenocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.60100000100000
411724118644marriedprofessional.coursenocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.61000000000010
411734118774marriedprofessional.coursenocellularnovfri23939991failure-1.194.767-50.81.0284963.60100000100000